Modern synthetic gene regulatory networks emerge from iterative design-build-test cycles that encompass the decisions and actions necessary to design, build, and test target genetic systems. Historically, such cycles have been performed manually, with limited formal problem-definition and progress-tracking. In recent years, researchers have devoted substantial effort to define and automate many sub-problems of these cycles and create systems for data management and documentation that result in useful tools for solving portions of certain workflows. However, biologists generally must still manually transfer information between tools, a process that frequently results in information loss. Furthermore, since each tool applies to a different workflow, tools often will not fit together in a closed-loop and, typically, additional outstanding sub-problems still require manual solutions. This thesis describes an attempt to create a tool that harnesses many smaller tools to automate a fully closed-loop decision-making process to design, build, and test synthetic biology networks and use the outcomes to inform redesigns. This tool, called Phoenix, inputs a performance-constrained signal-temporal-logic (STL) equation and an abstract genetic-element structural description to specify a design and then returns iterative sets of building and testing instructions. The user executes the instructions and returns the data to Phoenix, which then processes it and uses it to parameterize models for simulation of the behavior of compositional designs. A model-checking algorithm then evaluates these simulations, and returns to the user a new set of instructions for building and testing the next set of constructs. In cases where experimental results disagree with simulations, Phoenix uses grammars to determine where likely points of design failure might have occurred and instructs the building and testing of an intermediate composition to test where failures occurred. A design tree represents the design hierarchy displayed in the user interface where progress can be tracked and electronic datasheets generated to review results. Users can validate the computations performed by Phoenix by using them to create sets of classic and novel temporal synthetic genetic regulatory functions in E. coli. / 2016-12-31T00:00:00Z

The purpose of this thesis is to discuss the design and development of a platform used to automate a stock 2013 Ford Focus EV. The platform is low-cost and open-source to encourage collaboration and provide a starting point for fellow researchers to advance the work in the field of automated vehicle control. This thesis starts by discussing the process of obtaining control of the vehicle by taking advantage of internal communication protocols. The controller design process is detailed and a description of the components and software used to control the vehicle is provided. The automated system is tested and the results of fully autonomous driving are discussed.

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified. A normal hive inspection can be very disruptive for the bee colony, as the hive needs to be disassembled to visually assess hive health from the inside by collecting larvae and egg data. This work uses Machine Learning and Computer Vision methodologies to develop techniques to monitor hive health without disrupting the bee colony residing in the hive. Bee traffic refers to the number of bees moving in a given area in front of the hive over a given period of time. Bee traffic is related to forager traffic. Forager traffic is the number of bees moving out of the beehive. Forager traffic is a crucial factor in determining and monitoring food availability, food demand, colony age structure, the impact of pesticides, etc. on beehives. This work focuses on estimating bee traffic levels in a given hive and associate this information with data collected through manual beehive inspections.

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With autonomous vehicles becoming more common and established, there are some problems to overcome before their full potential can be reached. One of these problems is the lack of information about the condition of the road, which traditionally would be acquired from the driver operating the vehicle. Volvo Autonomous Solutions are developing an autonomous hauler, made for operating in off-road workplaces, such as quarries and mines. In these off-road workplaces, road maintenance is limited and often performed only when deemed necessary by a driver. This thesis investigates the issue of detecting irregularities in the road on an autonomous vehicle. To achieve this data from an Inertial Measurement Unit (IMU) and a Global Navigation Satellite System (GNSS) unit mounted on the vehicle is collected, analysed, and classified to find any irregularities in the road. In order to improve confidence in the classification of the irregularities, false positives are reduced by using an occupancy grid solution. The results show that the use of IMU data can be used to detect irregularities and that the use of an occupancy grid increases the confidence of detected irregularities.

A lot of features that need to be scanned underground should not be disturbed, from waterlines to unmarked graves. A non-invasive way of probing underground is Ground-Penetrating Radar (GPR). GPR finds differences in materials with radar waves. However, GPR is human-operated and its position is generally determined with a GPS. In some cases, the presence of a human operator can be dangerous, and in other cases, the GPS is not reliable (i.e. mines, glaciers). Therefore there are situations where an autonomous and non-GPS-reliant solution is preferable. The current state of the autonomous GPR system targeted in this work has a non-intuitive GUI that requires an experienced hand to operate. I present an updated hardware and software platform with an intuitive GUI. This updated autonomous system continuously builds a map of its surroundings with Simultaneous Mapping And Localization (SLAM). SLAM localizes itself within the map through sensor-fused position estimates. After the survey is completed the positions are saved and integrated with radar data to be visualized. Robot Operating System 2 (ROS2) is the software I used that allows communications between hardware components, software systems, and the GUI. The new hardware package uses only one source of power and is built using quick connectors that allow for quick removal from the GPR platform. This system allows for intuitive autonomous survey planning and execution in any field paired with a simple way of visualizing data.

This exploratory case study examined how and if career counselors learned about the impact of automation and artificial intelligence (AI) on occupations, including which actions, activities, and conditions helped or hindered their learning, and what impact this learning had on their professional practice. The data included questionnaires and interviews with 15 participants who worked as career counselors and expressed that learning about the impact of automation and AI on occupations was important in their work. Three primary findings emerged from the study. The first finding illustrated that participants primarily learned through informal and incidental learning and all participants described the lack of a clear path to learning. This was framed through four subthemes, including learning that emanated from reflection on “Aha!” Moments, learning that involved a process of continuous learning, formal learning that was lacking but desired, and participants stating that they did not seek out the learning. The second finding revealed that agency, openness, collaboration, and reflective practice helped learning, while limits to time, resources, and opportunities for learning hindered learning. The third finding showed that such learning impacted participants’ professional practice by affecting their interactions with clients and their professional development as career counselors. This demonstrated the application of the learning and connected it directly to the work of career counselors. This study contributes to the growing body of literature examining informal and incidental learning in complex and chaotic domains and provides recommendations for research and practice, specifically addressing how the results of this study can be used to support the professional development of career counselors in the Fourth Industrial Revolution.

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No description available.